A scalable chip multiprocessor for large-scale neural simulation

用于大规模神经模拟的可扩展芯片多处理器

基本信息

批准号：
EP/D07908X/1
负责人：
Stephen Furber
金额：
$ 81.27万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FD07908X%2F1
关键词：
scalable chip multiprocessor large scale

项目摘要

Biological brains are highly complex systems whose underlying principles of operation are little understood. We know that they comprise very large numbers of nerve cells - neurons - that interact with each other principally through electrical impulses or spikes, and we have instruments that can show which areas of the brain are more or less active at any time, but we know little about the intermediate levels of brain function. How, for example, are all the details of a complex visual scene encoded in the patterns of neural spikes in the visual cortex? And how do we use those patterns to recognize our family and friends?One way to help understand complex systems is to develop hypotheses of how those systems might work and then to use computers to test those hypotheses. Modelling spiking neurons is computationally very intensive, so a modern PC is capable of modelling a few tens of thousands of neurons in real time using a rather simple model of each neuron. In this research we plan to build a new sort of computer designed specifically for modelling large numbers of neurons in real time. This computer will be based upon large numbers of fairly simple microprocessors that communicate with each other using spike events modelled closely on the way biological neurons communicate. We will use developments in semiconductor technology to enable many microprocessors to be put on a single silicon chip, thereby keeping the cost and power consumption of the computer as low as possible.Our brains keep working despite frequent failures of their component neurons, and this fault-tolerant characteristic is of great interest to engineers who wish to make computers more reliable. So this work has two complementary ultimate goals: to use the computer to understand better how the brain works at the level of spike patterns, and to see if biology can help us see how to build computer systems that continue functioning despite component failures.

生物学大脑是高度复杂的系统，其基本的操作原理几乎没有理解。我们知道它们包含大量的神经细胞 - 神经元 - 它们主要通过电脉冲或尖峰相互作用，并且我们的仪器可以显示哪些或多或少的大脑区域在任何时候都在活跃，但我们对中间脑功能的中等水平了解。例如，如何按照视觉皮层中神经尖峰的模式编码复杂的视觉场景的所有细节？我们如何使用这些模式来识别我们的家人和朋友？帮助理解复杂系统的一种方法是开发有关这些系统如何工作的假设，然后使用计算机来检验这些假设。建模尖峰神经元在计算上非常密集，因此，现代PC能够使用每个神经元的相当简单的模型实时实时建模数万个神经元。在这项研究中，我们计划构建一种专门设计用于实时建模大量神经元的新计算机。该计算机将基于大量相当简单的微处理器，这些微处理器使用以生物神经元的交流方式建立了彼此交流的相互通信。我们将使用半导体技术中的开发项目，使许多微处理器能够使用一个硅芯片，从而使计算机的成本和功耗尽可能低。我们的大脑尽管经常出现其组件神经元的失败，并且这种容忍性的特征是使计算机变得更加可靠的工程师。因此，这项工作具有两个互补的最终目标：使用计算机更好地了解大脑在尖峰模式级别上的工作方式，并了解生物学是否可以帮助我们了解如何构建尽管发生故障的情况下继续运行的计算机系统。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Advances in Neuro-Information Processing

神经信息处理的进展

DOI：
10.1007/978-3-642-03040-6_127
发表时间：
2009
期刊：
影响因子：
0
作者：
Brown A
通讯作者：
Brown A

Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System.

DOI：
10.3389/fnins.2018.00434
发表时间：
2018
期刊：
Frontiers in neuroscience
影响因子：
4.3
作者：
Bogdan PA;Rowley AGD;Rhodes O;Furber SB
通讯作者：
Furber SB

Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.

DOI：
10.3389/fncel.2021.622870
发表时间：
2021
期刊：
Frontiers in cellular neuroscience
影响因子：
5.3
作者：
Bogdan PA;Marcinnò B;Casellato C;Casali S;Rowley AGD;Hopkins M;Leporati F;D'Angelo E;Rhodes O
通讯作者：
Rhodes O

SpiNNaker: Event-Based Simulation-Quantitative Behavior

SpiNNaker：基于事件的模拟定量行为